Click-through rate prediction in online advertising: A literature review

DHEN: A Deep and Hierarchical Ensemble Network for Large-Scale Click-Through Rate Prediction

“…This is by no means to enumerate all studies on interaction modules but provide representative examples. A more comprehensive review can be found in [45,49]. Although multiple aforementioned studies have shown the benefits of high-order interaction in prediction accuracy, a more in-depth look reveals that those high-order interactions are not equally important.…”

Causality-based CTR prediction using graph neural networks

“…In the literature on advertising CTR prediction, researchers have primarily explored four classes of modeling frameworks including multivariate statistical models, factorization machines (FMs) based models, deep learning models and tree models. For a comprehensive survey on CTR prediction models in online advertising, refer to see Yang & Zhai (2022).…”

“…As a prevalent problem in online advertising, CTR prediction has attracted plentiful research efforts from academia and industry. Existing CTR prediction research mainly focuses on feature interactions based on factorization machines (FMs), deep neural networks (DNNs) and graph neural networks (GNNs) (Yang & Zhai, 2022). Although FMs theoretically support high-order representations, FMs-based models typically use pairwise feature interactions for CTR prediction due to the complexity raised by high-order interactions (Rendle, 2010;Li et al, 2021b).…”

Keyword targeting optimization in sponsored search advertising: Combining selection and matching

“…In the SSA field, plentiful research efforts have been made to explore search auction mechanism design (e.g., Huang and Kauffman, 2011; and search user behavior analysis (e.g., Lo et al, 2014;Vragov et al, 2019;Lian et al 2021), empirical analysis of performance indices (e.g., Yang et al, 2018;Jeziorski and Moorthy, 2018;Schultz, 2020;Yang and Zhai, 2022), and advertising decisions including bidding optimization (e.g., Küç ükaydin et al, 2020;Kim et al, 2021), budget optimization (e.g., Yang et al, 2012;Yang and Xiong, 2020;Avadhanula et al, 2021;Yang et al, 2021b), and keyword optimization (e.g., Qiao et al, 2017;Nie et al, 2019;Scholz et al, 2019;Song et al, 2021;Zhang et al, 2021). This study focuses on one particular type of keyword decisions, i.e., keyword targeting, which draws from two research streams, namely keyword selection and keyword matching.…”